Common Obstacles to Analytics Success: The Business

What are the common hurdles encountered when putting analytics to work in a business, both in developing analytical models and applications and in building enterprise analytical capability?

This question is central to IIA’s mission of helping organizations navigate the many challenges to achieving analytics maturity.

Succeeding with analytics, and sustaining that success, is complex. It requires both a multi-pronged approach and an awareness of the pitfalls that analytics leaders and teams often face along the path toward increased analytics maturity.

In this blog series, we will focus on some of the biggest obstacles faced today. Our goal is to arm readers with specific warning signs, advice, and general food for thought about the state of their analytics efforts and things that can be done to improve the current state.

For our purposes, instead of summarizing by the common “people, process, technology” scheme, we will group obstacles into four categories more tightly tied to analytics:

• Business: Challenges that relate to business strategy, acumen, and management, including leadership and adoption of analytical outputs and insights
• Execution: Common challenges in the actual analytics work, including approaches to project work and model management
• Data and Technical: Obstacles that often require investments and strong partnerships with IT and technology vendors, including improving data platforms and analytic tooling
• People: Fundamental challenges facing both analytics and business individuals and teams, and the need for strong collaboration and partnership between them

For each category we’ll discuss warning signs and ways to overcome the obstacles. Our first installment explores business obstacles for success, like leadership challenges, prioritization, and adoption.

Business Obstacles for Success

1. Leadership: When Poor or Changing

Effective business leadership plays a pivotal role in advancing analytics initiatives. When leadership is strong and committed, progress accelerates. Conversely, a sudden loss of key leadership, especially if a sponsoring executive departs, can significantly hinder progress. It's not just about leadership claiming to support analytics; genuine commitment is key.

Leadership is the driving force behind transitioning from localized analytics to a more comprehensive, enterprise-wide approach. Mature analytical organizations have already made this transition, but they too face challenges when leadership changes or varies across business units.

Building and maintaining strong analytical leadership across the enterprise is an ongoing effort. Business leaders must stay engaged with analytics, recognizing its growing significance. Mature organizations employ two key strategies for enhancing leadership in analytics: forming analytics councils to involve various leaders in strategic planning and management, and leveraging analytically adept executives to mentor their peers in embracing analytics-based business capabilities. These approaches ensure that leadership remains a catalyst for analytical success.

Warning Signs:

Some of the common warning signs in this area include:

• Senior executives aren’t available when analytics managers and projects need them
• Executive attention to analytics strategies and initiatives wanes
• Executives delegate responsibility for analytics activities and participation in analytics councils

Overcoming Leadership Obstacles:

To sustain leadership and prevent obstacles, implement these four strategies:

• Analytics Councils: Expand analytics management by establishing an executive leadership council dedicated to enterprise analytics. These councils oversee analytics, educate participants, and ensure relevance through measurable objectives and charter renewals.
• Ongoing Stakeholder Engagement: Continuously assess stakeholders' alignment with analytics initiatives and develop succession plans for leadership roles. Encourage less-engaged executives to contribute as subject matter experts or reviewers.
• Measurable Business Objectives: Emphasize measuring business outcomes rather than solely focusing on model creation. Consistent measurement maintains executive interest and trust.
• Leadership by Example: Engage executives personally in analytics by involving them in scorecard development and encouraging continuous analytics usage in enterprise management.

2. Prioritization: Business Needs Not in Alignment with Analytics Workstreams

Today's strategic imperative emphasizes seamless alignment between analytics initiatives and overarching business strategy. While organizations have made strides in analytics maturity, a persistent gap often separates how analytics tasks are prioritized and broader business objectives.

This disconnect arises from analytics teams lacking a voice in high-level decision-making, the historical perception of analytics as a back-office function, exclusion from strategy development, and the challenge of translating business needs into actionable analytics projects. The fallout includes unsupported initiatives, missed opportunities, and diminished future investments in analytics.

Low ambition results in modest gains, while overly rapid analytics output can overwhelm and sow confusion. Achieving alignment is paramount.

To bridge this divide, analytics work and business strategy must intersect. Prioritization, technology investments, and resource allocation must align with business needs. Analytics capabilities should actively contribute to strategy development, aiding in forecasting, benchmarking, market analysis, and strategic optimization.

The challenge lies in orchestrating the harmonious advancement of technical and business analytics capabilities, ensuring a synchronized journey towards strategic alignment.

Warning Signs:

Some of the common warning signs in this area include:

• Businesspeople ask where the results are, what the strategy is, or why their needs aren’t being met
• The analytics group is faulted with being out of touch with business realities or priorities
• Analytics teams can’t articulate the connection between their work and the overarching business strategy

Overcoming Prioritization Obstacles:

Evaluate the strategic positioning of analytics in your company and the prioritization process for analytics work and ask yourself the following questions:

• What is the current-state utilization of analytics in strategic planning?
• Is there sufficient visibility within top-level business discussions of the value of analytics work to support business initiatives?
• Is there effective, strategy-minded prioritization of projects and investments?

Appointing a chief analytics officer, as many organizations have now done, can facilitate both the strategy alignment of analytics activities and the usage of analytics in strategy development.

3. Business Adoption: Resistance and Lack of Trust

Analytics can falter when businesspeople don't grasp them, leading to distrust and neglect. Resistance is another obstacle, stemming from a desire to keep traditional methods or doubts about new approaches. Surmounting resistance often requires nuanced change management, not just more training.

As organizations grow in analytical expertise, the “black box” issue recedes. More experts can explain models and decisions, and deployments offer comprehensive documentation. Yet, resistance can persist, especially as analytics become more embedded. Those accustomed to non-analytical decision-making may resist change. However, if analytics prove their worth, acceptance and trust usually follow.

Warning Signs:

Some of the common warning signs in this area include:

• Businesspeople question the analytics, the data, or the fit with their workflow and decision processes
• People say, “If it ain’t broke, don’t fix it” as an excuse to push back on new analytics
• Some analytics are completely unused or only partially used

Overcoming Common Adoption Obstacles:

It isn’t uncommon to see a solid analytical process struggle to get adoption. Although analysts might develop a workflow model showing when the analytics should be used, they might not effectively describe how the analytics work. Building a model of the incumbent decision-making process, and how the analytics replicate and improve that decision-making, can build trust and speed deployment. Other useful tips could include:

• Collaboration First: Involve business users and subject-matter experts from the start and encourage them to become advocates among their peers.
• Communication Excellence: Set high standards for analysts' communication, ensuring they explain processes clearly and incorporate audit trails for transparency.
• Rollout Preparedness: Anticipate challenges during deployment, especially in balancing human-machine interactions. Consider which decisions should be automated, recommended, or advisory and employ change management as needed for successful user engagement.